Tech: Python, Pandas, scikit-learn, Apache Airflow, Streamlit, Git.

Repo: https://github.com/Durianlychee/OpsPulse-Real-Time-KPI-Anomaly-Monitor

Author: Abdul Rahman Addakhili Ibrahim

Introduction

OpsPulse is a Python-based web dashboard and helper library for real-time KPI anomaly monitoring. It focuses on operational metrics (KPIs) and uses a machine-learning anomaly detector to flag unusual behavior before it turns into an incident. The project combines:

• A Streamlit dashboard for interactive exploration and visualization of anomalies

• A pipeline library (kpi_monitor) that handles data loading, anomaly detection, and alert formatting.

• An Airflow DAG for scheduled anomaly checks in a batch/orchestrated environment

Project Objectives

1. Make KPI anomaly detection easy to prototype- Ship a ready-to-run dataset and detector so you can validate ideas quickly.

2. Provide an interactive monitoring dashboard- Let operators and analysts slice KPIs by metric, region, and date range and visually inspect anomalies.

3. Demonstrate an end-to-end anomaly pipeline- From raw KPI data → detection → formatted alerts → scheduled runs via Airflow.

4. Serve as a scaffold for production integrations- Swap the CSV dataset with data warehouse queries or PostgreSQL and wire alert messages into Slack/email or other incident-management tools.

Example